Systematic Quant Trader
We are seeking a Systematic Quantitative Trader to design, implement, and manage alpha-driven, fully systematic strategies. Open to experience across equities, futures, and fixed income markets. This is a hands-on, front-office role where you will own the end-to-end lifecycle of strategies; Hypothesis generation and data acquisition to signal research, production deployment, live risk management, and ongoing performance optimization.
Responsibilities:
-Build predictive signals using a mix of statistical methods, machine learning, and econometric modeling, ensuring robustness via out-of-sample and adversarial validation.
-Translate alphas into well-specified portfolios using risk-aware portfolio construction (volatility targeting, dollar-neutral/cross-asset constraints, drawdown-aware sizing, and Kelly-fraction heuristics adjusted for estimation error).
-Partner with the execution team to design model-driven execution, aiming to minimize slippage, impact, and adverse selection.
-Own the full research-to-production pipeline: code quality, reproducibility, CI/CD, monitoring, alerting, and post-trade analytics.
-Partner with engineering to enhance data infrastructure (market, alternative, reference), backtesting frameworks, feature stores, and model serving.
Requirements:
3+ years of experience in systematic trading within a prop firm, hedge fund, or bank trading team, directly managing risk and capital.
A track record of live PnL and demonstrable risk discipline (2.0+ Sharpe).
Hands-on experience building and running systematic strategies end-to-end: data ingestion, signal research, portfolio construction, execution integration, and production monitoring.
Proficiency in Python (NumPy, Pandas, SciPy, scikit-learn), with experience in backtesting frameworks, research toolkits, and data engineering fundamentals.
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